{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7a/bf/04cfcfc9616cedd4b5dd24dfc40395965ea9f50c1db0d3f3e52b050f74a5/seaborn-0.9.0.tar.gz (198kB)\n",
      "\u001b[K    100% |################################| 204kB 9.7MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python2.7/dist-packages (from seaborn) (1.14.5)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python2.7/dist-packages (from seaborn) (1.1.0)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /usr/local/lib/python2.7/dist-packages (from seaborn) (0.23.4)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /usr/local/lib/python2.7/dist-packages (from seaborn) (2.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python2.7/dist-packages (from pandas>=0.15.2->seaborn) (2.7.3)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python2.7/dist-packages (from pandas>=0.15.2->seaborn) (2018.5)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib>=1.4.3->seaborn) (2.2.0)\n",
      "Requirement already satisfied: backports.functools-lru-cache in /usr/local/lib/python2.7/dist-packages (from matplotlib>=1.4.3->seaborn) (1.5)\n",
      "Requirement already satisfied: subprocess32 in /usr/local/lib/python2.7/dist-packages (from matplotlib>=1.4.3->seaborn) (3.5.2)\n",
      "Requirement already satisfied: six>=1.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib>=1.4.3->seaborn) (1.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python2.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (39.1.0)\n",
      "Building wheels for collected packages: seaborn\n",
      "  Running setup.py bdist_wheel for seaborn ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/fc/1c/74/c8f80a532c06a789599b8659b117ec7d7574cac4a06f7dabfe\n",
      "Successfully built seaborn\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.9.0\n",
      "\u001b[33mYou are using pip version 18.0, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "!pip install seaborn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('day.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5222b78ad0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f52229f0b10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f52229028d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f522293d690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f52228f53d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5222b42550>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5222976f50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f52208b5150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f522086d310>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['dteday','season','yr','mnth','holiday','weekday','workingday','weathersit']].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x4320 with 48 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a,b=df.shape[1],3\n",
    "_d=[]\n",
    "_,axes=plt.subplots(a,b,sharey=False,figsize=(10,60))\n",
    "for i in range(a):\n",
    "    for j in range(b):\n",
    "        _d.append(axes[i][j])\n",
    "k=0\n",
    "for name in df.columns:\n",
    "    if name not in ['instant','dteday']:\n",
    "        ax =  sns.boxplot(data=df[name],ax=_d[k],showmeans=True)\n",
    "        ax.set_title(name)\n",
    "        sns.violinplot(data=df[name],ax=_d[k+1],showmeans=True)\n",
    "        sns.countplot(df[name],ax=_d[k+2])\n",
    "        k+=3\n",
    "#使用py文件展示不是很理想，还没找到解决 控制子图大小对办法，在jupyter里面展示良好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6    ...      weathersit_1  weathersit_2  weathersit_3  \\\n",
       "0       0       0    ...                 0             1             0   \n",
       "1       0       0    ...                 0             1             0   \n",
       "2       0       0    ...                 1             0             0   \n",
       "3       0       0    ...                 1             0             0   \n",
       "4       0       0    ...                 1             0             0   \n",
       "\n",
       "   weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          0          1  \n",
       "1          1          0          0          0          0          0          0  \n",
       "2          0          1          0          0          0          0          0  \n",
       "3          0          0          1          0          0          0          0  \n",
       "4          0          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#通过观察，数据无缺失，cnt基本无噪音，不需要去噪音，需要对离散型数据 mnth  season weekday ,weathersit 做one hot处理\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for name in categorical_features:\n",
    "    df[name] =  df[name].astype('object')\n",
    "x_cat=df[categorical_features]\n",
    "x_cat=pd.get_dummies(x_cat)\n",
    "x_cat.head()\n",
    "#df.drop(categorical_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday season  yr mnth  holiday weekday  workingday  \\\n",
       "0        1  2011-01-01      1   0    1        0       6           0   \n",
       "1        2  2011-01-02      1   0    1        0       0           0   \n",
       "2        3  2011-01-03      1   0    1        0       1           1   \n",
       "3        4  2011-01-04      1   0    1        0       2           1   \n",
       "4        5  2011-01-05      1   0    1        0       3           1   \n",
       "\n",
       "  weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0          2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1          2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2          1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3          1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4          1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对temp atemp hum windspedd 去量纲\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "cate=['temp','atemp','hum','windspeed']\n",
    "_temp=MinMaxScaler().fit_transform(df[cate])\n",
    "x_cat_num=pd.DataFrame(data=_temp,columns=cate,index=df.index)\n",
    "x_cat_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6     ...      weekday_3  weekday_4  weekday_5  weekday_6  \\\n",
       "0       0       0     ...              0          0          0          1   \n",
       "1       0       0     ...              0          0          0          0   \n",
       "2       0       0     ...              0          0          0          0   \n",
       "3       0       0     ...              0          0          0          0   \n",
       "4       0       0     ...              1          0          0          0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  holiday  workingday  \n",
       "0  0.355170  0.373517  0.828620   0.284606        0           0  \n",
       "1  0.379232  0.360541  0.715771   0.466215        0           0  \n",
       "2  0.171000  0.144830  0.449638   0.465740        0           1  \n",
       "3  0.175530  0.174649  0.607131   0.284297        0           1  \n",
       "4  0.209120  0.197158  0.449313   0.339143        0           1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#组合数据\n",
    "X_train=pd.concat([x_cat,x_cat_num,df['holiday'],df['workingday']],axis=1,ignore_index=False)\n",
    "X_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...   weekday_5  weekday_6      temp     atemp       hum  \\\n",
       "0       0       0  ...           0          1  0.355170  0.373517  0.828620   \n",
       "1       0       0  ...           0          0  0.379232  0.360541  0.715771   \n",
       "2       0       0  ...           0          0  0.171000  0.144830  0.449638   \n",
       "3       0       0  ...           0          0  0.175530  0.174649  0.607131   \n",
       "4       0       0  ...           0          0  0.209120  0.197158  0.449313   \n",
       "\n",
       "   windspeed  holiday  workingday  yr   cnt  \n",
       "0   0.284606        0           0   0   985  \n",
       "1   0.466215        0           0   0   801  \n",
       "2   0.465740        0           1   0  1349  \n",
       "3   0.284297        0           1   0  1562  \n",
       "4   0.339143        0           1   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FE_train = pd.concat([df['instant'], X_train, df['yr'],df['cnt']], axis = 1)\n",
    "FE_train.to_csv('FE_day.csv', index=False)\n",
    "FE_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      "instant         731 non-null int64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "holiday         731 non-null int64\n",
      "workingday      731 non-null int64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.0 KB\n"
     ]
    }
   ],
   "source": [
    "FE_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据准备\n",
    "y=pd.DataFrame(FE_train,columns=['cnt'])\n",
    "X=FE_train.drop([\"instant\",\"cnt\"],axis=1)  #序号不是属于特征\n",
    "cate=FE_train.columns\n",
    "#分割数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=1,test_size=0.2)\n",
    "X_train.shape\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('r2 score of Lr test', 0.8495672617704251)\n",
      "('r2 score of Lr train', 0.8433423731553089)\n"
     ]
    }
   ],
   "source": [
    "#最小二乘线性回归模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import explained_variance_score\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "y_test_pred_lr=lr.predict(X_test)\n",
    "y_train_pred_lr=lr.predict(X_train)\n",
    "#print (lr.coef_,lr.intercept_)\n",
    "print ('explained_variance_score of Lr test',explained_variance_score(y_test,y_test_pred_lr))\n",
    "print ('explained_variance_score of Lr train',explained_variance_score(y_train,y_train_pred_lr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3000, 3000], [-8000, 8000], '--k')   #这样检查结果好像不对\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained_variance_scoreof RidgeCV on test is 0.8517213376557203\n",
      "explained_variance_scoreof RidgeCV on train is 0.8436106156871651\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 1.0)\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "ridge.fit(X_train, y_train) \n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "print 'explained_variance_scoreof RidgeCV on test is', explained_variance_score(y_test, y_test_pred_ridge)\n",
    "print 'explained_variance_scoreof RidgeCV on train is', explained_variance_score(y_train, y_train_pred_ridge)\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "print ('alpha is:', ridge.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The explained_variance_score  score of LassoCV on test is 0.8515476369457973\n",
      "The explained_variance_score score of LassoCV on train is 0.8439686549076898\n"
     ]
    }
   ],
   "source": [
    "# Lasso／L1正则\n",
    "from sklearn.linear_model import LassoCV\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "lasso = LassoCV(alphas=alphas)\n",
    "lasso.fit(X_train, y_train)  \n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "print 'The explained_variance_score  score of LassoCV on test is', explained_variance_score(y_test, y_test_pred_lasso)\n",
    "print 'The explained_variance_score score of LassoCV on train is', explained_variance_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>171.377044</td>\n",
       "      <td>[1.5488466678775788e+16]</td>\n",
       "      <td>[343.80762274062704]</td>\n",
       "      <td>weekday_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-369.765028</td>\n",
       "      <td>[1.5488466678775234e+16]</td>\n",
       "      <td>[-208.1151469521726]</td>\n",
       "      <td>weekday_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3.406771</td>\n",
       "      <td>[9347564446232820.0]</td>\n",
       "      <td>[176.6459842844838]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-493.160262</td>\n",
       "      <td>[9347564446232292.0]</td>\n",
       "      <td>[-312.33846007292595]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>44.498258</td>\n",
       "      <td>[6140902232542856.0]</td>\n",
       "      <td>[53.37204843644213]</td>\n",
       "      <td>weekday_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>36.373707</td>\n",
       "      <td>[6140902232542846.0]</td>\n",
       "      <td>[41.187857887848]</td>\n",
       "      <td>weekday_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[6140902232542796.0]</td>\n",
       "      <td>[7.112112520229971]</td>\n",
       "      <td>weekday_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-71.989970</td>\n",
       "      <td>[6140902232542713.0]</td>\n",
       "      <td>[-76.61636564061473]</td>\n",
       "      <td>weekday_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-153.294342</td>\n",
       "      <td>[6140902232542647.0]</td>\n",
       "      <td>[-160.74812899235258]</td>\n",
       "      <td>weekday_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2373.135060</td>\n",
       "      <td>[2395.0047343493097]</td>\n",
       "      <td>[1688.24852527976]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2051.953251</td>\n",
       "      <td>[2044.0567718856023]</td>\n",
       "      <td>[2054.442485605916]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1106.447472</td>\n",
       "      <td>[1136.6562987368497]</td>\n",
       "      <td>[1499.809389290021]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-1353.339529</td>\n",
       "      <td>[-1407.9033992168313]</td>\n",
       "      <td>[-1254.0853765891904]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1631.797281</td>\n",
       "      <td>[-1760.1583411888582]</td>\n",
       "      <td>[-1428.4743520727257]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>606.494383</td>\n",
       "      <td>[-2239775119643283.2]</td>\n",
       "      <td>[764.395955066306]</td>\n",
       "      <td>mnth_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>212.869051</td>\n",
       "      <td>[-2239775119643760.5]</td>\n",
       "      <td>[362.9696284506749]</td>\n",
       "      <td>mnth_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>122.364306</td>\n",
       "      <td>[-2239775119643836.5]</td>\n",
       "      <td>[238.9773463497861]</td>\n",
       "      <td>mnth_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.993158</td>\n",
       "      <td>[-2239775119643898.2]</td>\n",
       "      <td>[223.03162085702525]</td>\n",
       "      <td>mnth_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>69.078081</td>\n",
       "      <td>[-2239775119643898.5]</td>\n",
       "      <td>[274.26227989574716]</td>\n",
       "      <td>mnth_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-2239775119643956.5]</td>\n",
       "      <td>[93.94261680724435]</td>\n",
       "      <td>mnth_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-2239775119643982.2]</td>\n",
       "      <td>[91.89870676164401]</td>\n",
       "      <td>mnth_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-356.534195</td>\n",
       "      <td>[-2239775119644268.5]</td>\n",
       "      <td>[-133.9039632041795]</td>\n",
       "      <td>mnth_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-403.298970</td>\n",
       "      <td>[-2239775119644363.8]</td>\n",
       "      <td>[-339.1533134031879]</td>\n",
       "      <td>mnth_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-492.590087</td>\n",
       "      <td>[-2239775119644485.5]</td>\n",
       "      <td>[-442.08769561275767]</td>\n",
       "      <td>mnth_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-573.577471</td>\n",
       "      <td>[-2239775119644545.8]</td>\n",
       "      <td>[-521.4841995096795]</td>\n",
       "      <td>mnth_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-644.194667</td>\n",
       "      <td>[-2239775119644597.5]</td>\n",
       "      <td>[-612.8489824586188]</td>\n",
       "      <td>mnth_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>556.783351</td>\n",
       "      <td>[-3.5737752526783544e+16]</td>\n",
       "      <td>[643.293016109058]</td>\n",
       "      <td>season_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-3.5737752526784024e+16]</td>\n",
       "      <td>[90.72407350394906]</td>\n",
       "      <td>season_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-81.065026</td>\n",
       "      <td>[-3.5737752526784228e+16]</td>\n",
       "      <td>[3.375677287891449]</td>\n",
       "      <td>season_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-820.719755</td>\n",
       "      <td>[-3.573775252678485e+16]</td>\n",
       "      <td>[-737.3927669008967]</td>\n",
       "      <td>season_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>407.104034</td>\n",
       "      <td>[-3.951372584587446e+16]</td>\n",
       "      <td>[822.1800589206414]</td>\n",
       "      <td>weathersit_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-3.951372584587492e+16]</td>\n",
       "      <td>[375.4828151206457]</td>\n",
       "      <td>weathersit_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1573.891489</td>\n",
       "      <td>[-3.9513725845876504e+16]</td>\n",
       "      <td>[-1197.6628740412825]</td>\n",
       "      <td>weathersit_3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     coef_lasso                    coef_lr             coef_ridge  \\\n",
       "25   171.377044   [1.5488466678775788e+16]   [343.80762274062704]   \n",
       "19  -369.765028   [1.5488466678775234e+16]   [-208.1151469521726]   \n",
       "31     3.406771       [9347564446232820.0]    [176.6459842844838]   \n",
       "30  -493.160262       [9347564446232292.0]  [-312.33846007292595]   \n",
       "23    44.498258       [6140902232542856.0]    [53.37204843644213]   \n",
       "22    36.373707       [6140902232542846.0]      [41.187857887848]   \n",
       "24     0.000000       [6140902232542796.0]    [7.112112520229971]   \n",
       "21   -71.989970       [6140902232542713.0]   [-76.61636564061473]   \n",
       "20  -153.294342       [6140902232542647.0]  [-160.74812899235258]   \n",
       "26  2373.135060       [2395.0047343493097]     [1688.24852527976]   \n",
       "32  2051.953251       [2044.0567718856023]    [2054.442485605916]   \n",
       "27  1106.447472       [1136.6562987368497]    [1499.809389290021]   \n",
       "29 -1353.339529      [-1407.9033992168313]  [-1254.0853765891904]   \n",
       "28 -1631.797281      [-1760.1583411888582]  [-1428.4743520727257]   \n",
       "12   606.494383      [-2239775119643283.2]     [764.395955066306]   \n",
       "8    212.869051      [-2239775119643760.5]    [362.9696284506749]   \n",
       "13   122.364306      [-2239775119643836.5]    [238.9773463497861]   \n",
       "11     1.993158      [-2239775119643898.2]   [223.03162085702525]   \n",
       "9     69.078081      [-2239775119643898.5]   [274.26227989574716]   \n",
       "6      0.000000      [-2239775119643956.5]    [93.94261680724435]   \n",
       "7     -0.000000      [-2239775119643982.2]    [91.89870676164401]   \n",
       "10  -356.534195      [-2239775119644268.5]   [-133.9039632041795]   \n",
       "14  -403.298970      [-2239775119644363.8]   [-339.1533134031879]   \n",
       "5   -492.590087      [-2239775119644485.5]  [-442.08769561275767]   \n",
       "15  -573.577471      [-2239775119644545.8]   [-521.4841995096795]   \n",
       "4   -644.194667      [-2239775119644597.5]   [-612.8489824586188]   \n",
       "3    556.783351  [-3.5737752526783544e+16]     [643.293016109058]   \n",
       "1      0.000000  [-3.5737752526784024e+16]    [90.72407350394906]   \n",
       "2    -81.065026  [-3.5737752526784228e+16]    [3.375677287891449]   \n",
       "0   -820.719755   [-3.573775252678485e+16]   [-737.3927669008967]   \n",
       "16   407.104034   [-3.951372584587446e+16]    [822.1800589206414]   \n",
       "17    -0.000000   [-3.951372584587492e+16]    [375.4828151206457]   \n",
       "18 -1573.891489  [-3.9513725845876504e+16]  [-1197.6628740412825]   \n",
       "\n",
       "         columns  \n",
       "25     weekday_6  \n",
       "19     weekday_0  \n",
       "31    workingday  \n",
       "30       holiday  \n",
       "23     weekday_4  \n",
       "22     weekday_3  \n",
       "24     weekday_5  \n",
       "21     weekday_2  \n",
       "20     weekday_1  \n",
       "26          temp  \n",
       "32            yr  \n",
       "27         atemp  \n",
       "29     windspeed  \n",
       "28           hum  \n",
       "12        mnth_9  \n",
       "8         mnth_5  \n",
       "13       mnth_10  \n",
       "11        mnth_8  \n",
       "9         mnth_6  \n",
       "6         mnth_3  \n",
       "7         mnth_4  \n",
       "10        mnth_7  \n",
       "14       mnth_11  \n",
       "5         mnth_2  \n",
       "15       mnth_12  \n",
       "4         mnth_1  \n",
       "3       season_4  \n",
       "1       season_2  \n",
       "2       season_3  \n",
       "0       season_1  \n",
       "16  weathersit_1  \n",
       "17  weathersit_2  \n",
       "18  weathersit_3  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coef_lr和coef_ridge里面有参数非常大，应该是哪里出错了。 相比之下coef_lasso 稍微合理，没有过拟合"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
